AI-based prediction of the results of bulk forming simulations

体积成形过程有限元模拟的软件和硬件已经发展成熟。然而，在锻造零件的产品和工艺开发中使用有限元模拟的特点是仍然需要很长时间的计算工作。该项目的目标是开发一种预测方法，该方法允许在不干扰设计师的时间段内对CAD环境中形成模拟的结果进行足够详细的预测。这意味着相关系数应该大于90%，预测时间应该短于60秒。在该项目中，人工智能（AI）算法将用于预测模拟结果。结果的特征在于例如所需的成形力、型腔的填充或成品零件的几何形状。选定的数据挖掘（DM）算法进行训练和评估的FE模拟（约50,000记录）的结果。 因此，E。G.必须开发可由DM算法解释的几何模型。初步研究表明了该创新方法的可行性。研究结果将有助于减少设计和有限元计算之间的迭代次数到必要的最低限度。

Software and hardware for the finite-element-method(FEM)-simulation of bulk forming processes are well developed. However the utilization of FEM-simulation in the product and process development of forged parts is characterized by a still long time effort for the calculation. The projects objective is the development of a prediction method, which allows a sufficiently detailed forecast of the results of forming simulations out of a CAD environment in a time period that does not disturb the designer. That means e.g. the correlation coefficient should be bigger than 90 percent and the prediction time should be shorter than 60 seconds. In the project algorithms of artificial intelligence (AI) will be used to predict the result of the simulations. The results are characterized e.g. by the required forming force, the filling of the cavity or the geometry of the finished part. Selected data mining (DM) algorithms are trained and evaluated with the results of FE simulations (approximately 50,000 records). Therefore e. g. a geometry model has to be developed that can be interpreted by the DM algorithms. The feasibility of the innovative approach was shown by preliminary investigations. The research results will help to reduce the number of iterations between design and time consuming FEM-calculation to a necessary minimum.